A model for within-group coalitionary aggression among males

Perhaps the most common form of cooperation among primates is the formation of coalitions. Competition among males within a group concerns a constant quantity of the limiting resource (fertilizations). Contest competition over fertilizations is known to produce payoffs that are distributed according to the priority-of-access model, and hence show an exponential decline in payoff with rank. We develop a model for rank-changing, within-group coalitions among primate males. For these coalitions to occur, they must be both profitable (i.e. improve fitness) for all coalition members and feasible (i.e. be able to beat the targets). We assume that the value of the coalition is the sum of the payoffs of the partners in their original ranks. We distinguish three basic coalition configurations, depending on the dominance ranks of the coalition partners relative to their target. We predict five basic coalition types. First, all-up, rank-changing coalitions targeting individuals ranking above all coalition partners; these are expected to involve coalition partners ranking just below their target, concern top rank, and be small, just two or three animals. Second, bridging, rank-changing coalitions, where higher-rankers support lower-rankers to rise to a rank below themselves; these are expected to be most common where a high-ranking male in a despotic system can support a low-ranking relative. Third, bridging non-rank-changing coalitions; these are expected to be common whenever high-ranking males have low-ranking close relatives. Fourth, non-rank-changing coalitions by high-rankers against lower-ranking targets; these are expected to serve to counteract or prevent the first type. Fifth, non-rank-changing, leveling coalitions, in which all partners rank below their target and which flatten the payoff distribution; these are expected to be large and mainly involve lower-ranking males. Bridging, rank-changing coalitions are expected in situations where contest is strong, all-up rank-changing coalitions where contest is intermediate, and leveling coalitions where contest is weak. We review the empirical patterns found among primates. The strong predictions of the model are confirmed by observational data on male-male coalitions in primates.

The evolution of female copulation calls in primates: a review and a new model

Female copulation calls are mating-associated vocalizations that occur in some species of Old World monkeys and apes. We argue that copulation calls have two immediate functions: to encourage mating attempts by other males and to increase mate guarding by the consort male. We hypothesize that female copulation calls have evolved under the selective pressures of risk of infanticide and sperm competition. When male mate guarding is effective, copulation calls allow females to concentrate paternity in dominant males and benefit from their protection against the risk of infanticide. When mate guarding is ineffective, copulation calls may bring genetic benefits to females through facilitation of sperm competition. We present a quantitative model in which interspecific variation in females' promiscuity predicts their tendency to use copulation calls in conjunction with mating. The model predicts that in species with little female promiscuity, copulation calls should be rare and exhibited only in association with mating with dominant males. In species in which females are highly promiscuous, copulation calls should be frequent and unrelated to male dominance rank. The limited data available to test the model support its main predictions as well as the predicted relation between copulation calls and male dominance rank.

Hierarchical modeling for extreme values observed over space and time

We propose a hierarchical modeling approach for explaining a collection of spatially referenced time series of extreme values. We assume that the observations follow generalized extreme value (GEV) distributions whose locations and scales are jointly spatially dependent where the dependence is captured using multivariate Markov random field models specified through coregionalization. In addition, there is temporal dependence in the locations. There are various ways to provide appropriate specifications; we consider four choices. The models can be fitted using a Markov Chain Monte Carlo (MCMC) algorithm to enable inference for parameters and to provide spatio–temporal predictions. We fit the models to a set of gridded interpolated precipitation data collected over a 50-year period for the Cape Floristic Region in South Africa, summarizing results for what appears to be the best choice of model.

A stochastic forest fire growth model

We consider a stochastic fire growth model, with the aim of predicting the behaviour of large forest fires. Such a model can describe not only average growth, but also the variability of the growth. Implementing such a model in a computing environment allows one to obtain probability contour plots, burn size distributions, and distributions of time to specified events. Such a model also allows the incorporation of a stochastic spotting mechanism.

Testing homogeneity of capture probabilities of animals in a removal experiment

Consider the removal experiment used to estimate population sizes. Statistical methods towards testing the homogeneity of capture probabilities of animals, including a graphical diagnostic and a formal test, are presented and illustrated by real biological examples. Simulation is used to assess the test and compare it with the χ² test.

Behavior of yellowfin (<Emphasis Type="Italic">Thunnus albacares</Emphasis>) and bigeye (<Emphasis Type="Italic">T. obesus</Emphasis>) tuna in a network of fish aggregating devices (FADs)

The influence of multiple anchored fish aggregating devices (FADs) on the spatial behavior of yellowfin (Thunnus albacares) and bigeye tuna (T. obesus) was investigated by equipping all thirteen FADs surrounding the island of Oahu (HI, USA) with automated sonic receivers (“listening stations”) and intra-peritoneally implanting individually coded acoustic transmitters in 45 yellowfin and 12 bigeye tuna. Thus, the FAD network became a multi-element passive observatory of the residence and movement characteristics of tuna within the array. Yellowfin tuna were detected within the FAD array for up to 150 days, while bigeye tuna were only observed up to a maximum of 10 days after tagging. Only eight yellowfin tuna (out of 45) and one bigeye tuna (out of 12) visited FADs other than their FAD of release. Those nine fish tended to visit nearest neighboring FADs and, in general, spent more time at their FAD of release than at the others. Fish visiting the same FAD several times or visiting other FADs tended to stay longer in the FAD network. A majority of tagged fish exhibited some synchronicity when departing the FADs but not all tagged fish departed a FAD at the same time: small groups of tagged fish left together while others remained. We hypothesize that tuna (at an individual or collective level) consider local conditions around any given FAD to be representative of the environment on a larger scale (e.g., the entire island) and when those conditions become unfavorable the tuna move to a completely different area. Thus, while the anchored FADs surrounding the island of Oahu might concentrate fish and make them more vulnerable to fishing, at a meso-scale they might not entrain fish longer than if there were no (or very few) FADs in the area. At the existing FAD density, the ‘island effect’ is more likely to be responsible for the general presence of fish around the island than the FADs. We recommend further investigation of this hypothesis.

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A note on data augmentation and iterative simulation for survival rate estimation

In this paper we examine the use of data augmentation techniques for simplifying iterative simulation in the context of both Bayesian and classical statistical inference for survival rate estimation. We examine two distinct model families common in population ecology to illustrate our ideas, ring-recovery models and capture–recapture models, and we present the computational advantage of this approach. We discuss also the fact that problems associated with identifiability in the classical framework can be overcome using data augmentation, but highlight the dangers in doing so under both inferential paradigms.

Hierarchical spatial point process analysis for a plant community with high biodiversity

A complex multivariate spatial point pattern of a plant community with high biodiversity is modelled using a hierarchical multivariate point process model. In the model, interactions between plants with different post-fire regeneration strategies are of key interest. We consider initially a maximum likelihood approach to inference where problems arise due to unknown interaction radii for the plants. We next demonstrate that a Bayesian approach provides a flexible framework for incorporating prior information concerning the interaction radii. From an ecological perspective, we are able both to confirm existing knowledge on species’ interactions and to generate new biological questions and hypotheses on species’ interactions.

Confidence limits on one-stage model parameters in benchmark risk assessment

In modern environmental risk analysis, inferences are often desired on those low dose levels at which a fixed benchmark risk is achieved. In this paper, we study the use of confidence limits on parameters from a simple one-stage model of risk historically popular in benchmark analysis with quantal data. Based on these confidence bounds, we present methods for deriving upper confidence limits on extra risk and lower bounds on the benchmark dose. The methods are seen to extend automatically to the case where simultaneous inferences are desired at multiple doses. Monte Carlo evaluations explore characteristics of the parameter estimates and the confidence limits under this setting.

Flexible hierarchical mark-recapture modeling for open populations using WinBUGS

Hierarchical mark-recapture models offer three advantages over classical mark-recapture models: (i) they allow expression of complicated models in terms of simple components; (ii) they provide a convenient way of modeling missing data and latent variables in a way that allows expression of relationships involving latent variables in the model; (iii) they provide a convenient way of introducing parsimony into models involving many nuisance parameters. Expressing models using the complete data likelihood we show how many of the standard mark-recapture models for open populations can be readily fitted using the software WinBUGS. We include examples that illustrate fitting the Cormack–Jolly–Seber model, multi-state and multi-event models, models including auxiliary data, and models including density dependence.

Reciprocal altruism and food sharing decisions among Hiwi and Ache hunter–gatherers

The common occurrence of food transfers within human hunter–gatherer and forager–horticulturalist groups presents exciting test cases for evolutionary models of altruism. While kin biases in sharing are consistent with nepotism based on kin selection, there is much debate over the extent to which reciprocal altruism and tolerated scrounging provide useful explanations of observed behavior. This paper presents a model of optimal sharing breadth and depth, based on a general non-tit-for-tat form of risk-reduction based reciprocal altruism, and tests a series of predictions using data from Hiwi and Ache foragers. I show that large, high variance food items are shared more widely than small, easily acquired food items. Giving is conditional upon receiving in pairwise interactions and this correlation is usually stronger when the exchange of value rather than quantities is considered. Larger families and low producing families receive more and give less, consistent with the notion that marginal value may be a more salient currency than quantity.

Is bird song a reliable signal of aggressive intent?

In this commentary, we discuss recent experiments on the reliability of bird song as a signal of aggressive intent during territorial conflicts. We outline relevant theoretical views on honest signaling, highlighting the vulnerability handicap hypothesis as a possible explanation for soft song’s reliability in predicting attack. We also sketch possible methods of testing whether soft song agrees with key predictions of the vulnerability handicap hypothesis. Finally, we suggest possible empirical refinements that may be useful in future studies of signals of intent, both in birds and in animals broadly. In particular, we argue that future studies of intent should strive to incorporate the following elements into their experimental design: (1) multi-modal signal components, (2) interaction dynamics, and (3) minimal time intervals. Simulated exchanges using dynamically interactive models may provide a powerful means of incorporating all three of these design features simultaneously.

Maximum likelihood fitting of the Poisson lognormal distribution

In this paper some properties and analytic expressions regarding the Poisson lognormal distribution such as moments, maximum likelihood function and related derivatives are discussed. The author provides a sharp approximation of the integrals related to the Poisson lognormal probabilities and analyzes the choice of the initial values in the fitting procedure. Based on these he describes a new procedure for carrying out the maximum likelihood fitting of the truncated Poisson lognormal distribution. The method and results are illustrated on real data. The computer program for calculations is freely available.

Vertical distribution of macroplankton at the Charlie-Gibbs Fracture Zone (North Atlantic), as observed from the manned submersible “Mir-1”

Direct observations of the plankton vertical distribution performed from a manned submersible were made in the Charlie-Gibbs Fracture Zone (Atlantic Ocean). Plankton was counted using a standard 3-m² frame in the depth layer from 170 to 4361 m and plotted for every 50 m. The most abundant components of the macroplankton were pelagic shrimps, chaetognaths and gelatinous animals, with peak of densities corresponding to the main pycnocline. Mucous houses of appendicularians were abundant at 150 m above the seabed—up to 0.07–0.09 m^–3, compared to 0–0.006 m^–3 in the upper layers. The new observations confirm that the near-bottom peak of appendicularian abundance is characteristic of at least the entire Central Atlantic, and it is not associated with certain biotopes on the bottom.Communicated by O. Kinne, Oldendorf/Luhe

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Determining the optimum number of increments in composite sampling

Composite sampling can be more cost effective than simple random sampling. This paper considers how to determine the optimum number of increments to use in composite sampling. Composite sampling terminology and theory are outlined and a method is developed which accounts for different sources of variation in compositing and data analysis. This method is used to define and understand the process of determining the optimum number of increments that should be used in forming a composite. The blending variance is shown to have a smaller range of possible values than previously reported when estimating the number of increments in a composite sample. Accounting for differing levels of the blending variance significantly affects the estimated number of increments.

Trap mortality in mark-recapture studies

When animals die in traps in a mark-recapture study, straightforward likelihood inferences are possible in a class of models. The class includes M₀, M_t, and M_b as reported by White et al. (Los Alamos National Laboratory, LA-8787-NERP, pp 235, 1982), those that do not involve heterogeneity. We include three Markov chain “persistence” models and show that they provide good fits in a trapping study of deer mice in the Cascade-Siskiyou National Monument of Southern Oregon where trapping mortality was high.

A spatial clustering perspective on autocorrelation and regionalization

We revisit one of the classical problems in geography and cartography where multiple observations on a lattice (N) need to be grouped into many fewer regions (G), especially when this number of desired regions is unknown a priori. Since an optimization through all possible aggregations is not feasible, a hierarchical classification scheme is proposed with an objective function sensitive to spatial pattern. The objective function to be minimized during the assignment of observations to regions (classification) consists of two terms: the first characterizes accuracy and the second, model complexity. For the latter, we introduce a spatial measure that characterizes the number of homogeneous patches rather than the usual number of classes. A simulation study shows that such a classification procedure is less sensitive to random and spatially correlated error (noise) than non-spatial classification. We also show that for conditional autoregressive error (noise) fields the optimal partitioning is the one that has the highest within-units generalized Moran coefficient. The classifier is implemented in ArcView to demonstrate both a socio-economic and an environmental application to illustrate some potential applications.

Fishery resource management in Chilika lagoon: a study on coastal conservation in the Eastern Coast of India

A significant proportion of the fishing population comprises small-scale fishermen and many studies illustrate that these people are exploited by middlemen in the process of fish marketing combined with money lending. The negative dependency gives rise to poverty and triggers indiscriminate fish catch that threatens fishery resources depletion. This article explores the root causes of failures in resource-led development from the viewpoint of coastal resource conservation. The study presents a case study of Chilika lagoon, India and focuses on the interaction between small-scale fishermen and middlemen. The findings reveal that most of the small-scale fishermen have been exploited by specific middlemen and the underlying causes of the present fish marketing structure stem from (i) indebtedness and (ii) the unstable situation because of perpetual conflicts over fishery resources among the fishers across Chilika lagoon. Based on these observations, this article presents some recommendations on fishery resource conservation from the perspective of a fish marketing structure.

A note on non-parametric and semi-parametric modeling of wildfire hazard in Los Angeles County, California

This paper explores the use of, and problems that arise in, kernel smoothing and parametric estimation of the relationships between wildfire incidence and various meteorological variables. Such relationships may be treated as components in separable point process models for wildfire activity. The resulting models can be used for comparative purposes in order to assess the predictive performance of the Burning Index.

Improved strategies for coverage estimation by using replicated line-intercept sampling

Coverage, i.e., the area covered by the target attribute in the study region, is a key parameter in many surveys. Coverage estimation is usually performed by adopting a replicated protocol based on line-intercept sampling coupled with a suitable linear homogeneous estimator. Since coverage is a parameter which may be interestingly represented as the integral of a suitable function, improved Monte Carlo strategies for implementing the replicated protocol are introduced in order to achieve estimators with small variance rates. In addition, new specific theoretical results on Monte Carlo integration methods are given to deal with the integrand functions arising in the special coverage estimation setting.